BIOLOGICAL AGING AND APOE STATUS REWIRE INTER-OMIC ASSOCIATIONS RELATED TO BIOENERGETICS IN HUMANS

Abstract Apolipoprotein E (APOE) modifies human aging, with the ε2 and ε4 alleles being among the strongest genetic predictors of longevity and Alzheimer’s disease, respectively. However, the mechanisms of APOE’s impact on aging and cognition remain largely uncharacterized. In this study, we analyzed inter-omic context-dependent association patterns across APOE genotype, sex, and health in an undiagnosed cohort of 1950 individuals. We hypothesized that APOE genotypes would show variation in energy metabolites tied to previously-validated metrics of ’biological aging’, a modifiable health metric based on blood biomarkers. Our analysis identified top APOE-associated metabolites as diacylglycerols, including oleoyl- and linoleoyl-arachidonoyl-glycerols, similarly increased in APOE ε2- and ε4- carriers compared to ε3-homozygotes. Male ε2-carriers and biologically-older males displayed a similar increase in associations between insulin resistance and bioenergetic metabolites including pyruvate, glucose, gluconate, and lactate, a trend which was validated in an independent cohort of TwinsUK females. These results provide an atlas of APOE allele-rewired associations and support the involvement of bioenergetic pathways in mediating APOE impact on longevity and AD risk, suggesting targets for enhancing healthspan via lifestyle-modifications or drug-repurposing.

Background: Few studies have examined the characteristics of individuals who experienced unmet care needs during the COVID-19 pandemic (a time when more needs may have gone unmet).Methods: Using 2020 data from the Health and Retirement Study, this study selected a sample of respondents over the age of 50 who reported difficulty with completing activities of daily living (ADL; eating, bathing, dressing, toileting, toileting; N=685).Unmet ADL need (outcome) was defined as having difficulty performing one or more ADLs but not receiving help performing that ADL.A logistic regression analyzed predictors of unmet ADL need, using the predictors of cognitive ability (range 0-27; Crimmins et al., 2011), selfrated eyesight, self-rated hearing, education, age, gender, race, marital status, and having a child living within 10 miles.Findings: Females were less likely to report an unmet ADL need (OR=0.66,p=.03), as were married/partnered respondents (OR=.52,p<.01).In contrast, respondents with higher cognitive ability were more likely to report unmet ADL needs (OR=1.04,p=.02).The remaining predictors were not statistically significant.Discussion: Having difficulty with basic ADLs and lacking assistance through formal or informal caregiving can increase the risk for a serious adverse event or nursing home placement.While those with higher cognitive ability could make use of creative workarounds (e.g., assistive devices); they may also have been perceived as having less need of assistance from others.By identifying groups who are more likely to have unmet ADL needs, outreach, assessment, and intervention outreach efforts can be targeted to those at greatest risk.

METABOLOMICS OF LONGEVITY AND LIFESPAN
Chair: Paola Sebastiani Discussant: Nalini Raghavachari Serum metabolomics has been an important source of biomarkers of aging and longevity for years.This symposium will bring together investigators from large studies of human longevity to provide an overview of recent discoveries on serum metabolomics of aging and extreme human longevity, their connections to genetic variations, and highlight the challenges of correlating metabolomic profiles of aging in human studies and across multiple species.Dr. Sebastiani will describe results from analyses of serum metabolomics of participants enrolled in the Long Life Family Study, highlight similarities and differences between metabolomic profiles of old age and extreme old age, and some connections with genetics of extreme human longevity.Dr. Rappaport will connect specific variations of the APOE alleles to metabolomic profiles and describe a possible role of bioenergetics pathways in mediating the effect of APOE to longevity and resistance to Alzheimer's disease.Dr. Monti will expand the characterization of metabolomics of aging and extreme human longevity in a large metabolomic study of very old centenarians by using traditional statistical analyses and novel machine learning techniques.His analysis identifies rich signatures of aging and longevity that include well known metabolites and point to bile acids and several classes of steroids as important marker of longevity.Analytical innovations will be taken further by Dr. Schork who will introduce a novel approach based on distance of profiles to analyze multiple metabolites simultaneously and show the value of this approach to analyze metabolomic profiles of maximum lifespan across multiple species.This is a Geroscience Interest Group Sponsored Symposium.(2) a signature of extreme old age that differs from the age-related signature; and (3) a signature that predict survival.We analyzed 409 general metabolites and lipid species in approximately 2700 LLFS participants and used a state-of-the-art computational approach with mixed effect models and whole genome sequence data to model within family relation.The analysis identified 305 metabolites that correlate with age at 5% false discovery rate (FDR), 30 metabolites that are not age related and differ in centenarians compared to younger individuals, and 144 metabolites that predict survival at 5%FDR.The aging signature included well known markers: eg ergothioneine and tryptophan that decreased with older age, and was enriched for carbohydrates, organic acids, and several lipid classes.The extreme old age signature was enriched for glycerolipids and glycerophospholipids.The metabolomics signature of survival was enriched for nucleic and organic acids.Comparison with other studies showed strong agreement of results and also highlighted unique finding in extreme old individuals.The analysis also showed substantial variability of serum metabolomics at different ages, thus confirming the heterogeneity of molecular signatures of age and the opportunity to discover specific molecular profiles that promote heathy aging.
Apolipoprotein E (APOE) modifies human aging, with the ε2 and ε4 alleles being among the strongest genetic predictors of longevity and Alzheimer's disease, respectively.However, the mechanisms of APOE's impact on aging and cognition remain largely uncharacterized.In this study, we analyzed interomic context-dependent association patterns across APOE genotype, sex, and health in an undiagnosed cohort of 1950 individuals.We hypothesized that APOE genotypes would show variation in energy metabolites tied to previouslyvalidated metrics of 'biological aging', a modifiable health metric based on blood biomarkers.Our analysis identified top APOE-associated metabolites as diacylglycerols, including oleoyl-and linoleoyl-arachidonoyl-glycerols, similarly increased in APOE ε2-and ε4-carriers compared to ε3-homozygotes.Male ε2-carriers and biologically-older males displayed a similar increase in associations between insulin resistance and bioenergetic metabolites including pyruvate, glucose, gluconate, and lactate, a trend which was validated in an independent cohort of TwinsUK females.These results provide an atlas of APOE allele-rewired associations and support the involvement of bioenergetic pathways in mediating APOE impact on longevity and AD risk, suggesting targets for enhancing healthspan via lifestylemodifications or drug-repurposing.

. Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, United States
A total of 1,495 chemicals including 1,213 compounds of known identity and 282 compounds of unknown structural identity were profiled in serum samples collected at enrollment at Metabolon, Inc. from the blood serum of 213 subjects, including centenarians (n=80), offspring (n=70), and controls (n=63), mean ages of 105, 70, and 70, respectively, enrolled in the New England Centenarian Study (NECS).We performed metabolite-and metabolite module-based regression analyses on age, differential analyses comparing centenarian to offspring and control, and Cox proportional hazard-based survival analyses.We annotated the derived signatures by enrichment analysis using metabolite sets curated by Metabolon, RefMet, and Pathbank, among others.We identified 323 (436) metabolites that change with age at an FDR-corrected q-value of 0.01 (0.05), and 298 (407) metabolites differentially abundant between centenarians, their younger offspring, and controls.Our results confirm and expand upon previous metabolomics-based studies of aging, including decreased abundance with age of Tryptophan, DHEA-S, Pregnenolone, Glutathione, and Androgen, among others, and increased abundance of Amino acids (Tyrosines), Phenylacetylglutamine, Ornithine, Urea, Kynurenine, and Creatine, among others.To distinguish metabolites associated with longevity from those associated with extreme old age, we performed age-adjusted survival analysis, and rank-based enrichment analysis identified several metabolites significantly associated with survival independent of age, including secondary and primary (C 24) bile acids, and multiple classes of steroids (androgens, pregnenolone, progestin), among others.Additional analyses are ongoing to further annotate and characterize these findings.If validated, these results could contribute to the identification of novel healthy aging therapeutics.
Abstract citation ID: igad104.2060 St. Louis, Missouri, United States Serum metabolomics of aging has been a growing area of interest and several studies have identified metabolites that correlate with chronological age.The Long Life Family Study (LLFS) has generated serum metabolomics of individuals aged 30 to 110 years and several years of follow up, and offers a unique opportunity to identify (1) a robust signature of age;